Add missing documentation (#55)

etna/models/catboost.py

@@ -57,6 +57,45 @@ def __init__(
         thread_count: int = 4,
         **kwargs,
     ):
+        """Create instance of CatBoostModelPerSegment with given parameters.
+
+        Parameters
+        ----------
+        iterations:
+            The maximum number of trees that can be built when solving
+            machine learning problems. When using other parameters that
+            limit the number of iterations, the final number of trees
+            may be less than the number specified in this parameter.
+        depth:
+            Depth of the tree. The range of supported values depends
+            on the processing unit type and the type of the selected loss function:
+            CPU — Any integer up to  16.
+            GPU — Any integer up to 8 pairwise modes (YetiRank, PairLogitPairwise and
+            QueryCrossEntropy) and up to   16 for all other loss functions.
+        learning_rate:
+            The learning rate. Used for reducing the gradient step.
+        logging_level:
+            The logging level to output to stdout.
+            Possible values:
+            Silent — Do not output any logging information to stdout.
+            Verbose — Output the following data to stdout:
+                optimized metric
+                elapsed time of training
+                remaining time of training
+            Info — Output additional information and the number of trees.
+            Debug — Output debugging information.
+        l2_leaf_reg:
+            Coefficient at the L2 regularization term of the cost function.
+            Any positive value is allowed.
+        thread_count:
+            The number of threads to use during the training.
+            For CPU
+            Optimizes the speed of execution. This parameter doesn't affect results.
+            For GPU
+            The given value is used for reading the data from the hard drive and does
+            not affect the training.
+            During the training one main thread and one thread for each GPU are used.
+        """
         super(CatBoostModelPerSegment, self).__init__(
             base_model=_CatBoostModel(
                 iterations=iterations,
@@ -83,6 +122,45 @@ def __init__(
         thread_count: int = 4,
         **kwargs,
     ):
+        """Create instance of CatBoostModelMultiSegment with given parameters.
+
+        Parameters
+        ----------
+        iterations:
+            The maximum number of trees that can be built when solving
+            machine learning problems. When using other parameters that
+            limit the number of iterations, the final number of trees
+            may be less than the number specified in this parameter.
+        depth:
+            Depth of the tree. The range of supported values depends
+            on the processing unit type and the type of the selected loss function:
+            CPU — Any integer up to  16.
+            GPU — Any integer up to 8 pairwise modes (YetiRank, PairLogitPairwise and
+            QueryCrossEntropy) and up to   16 for all other loss functions.
+        learning_rate:
+            The learning rate. Used for reducing the gradient step.
+        logging_level:
+            The logging level to output to stdout.
+            Possible values:
+            Silent — Do not output any logging information to stdout.
+            Verbose — Output the following data to stdout:
+                optimized metric
+                elapsed time of training
+                remaining time of training
+            Info — Output additional information and the number of trees.
+            Debug — Output debugging information.
+        l2_leaf_reg:
+            Coefficient at the L2 regularization term of the cost function.
+            Any positive value is allowed.
+        thread_count:
+            The number of threads to use during the training.
+            For CPU
+            Optimizes the speed of execution. This parameter doesn't affect results.
+            For GPU
+            The given value is used for reading the data from the hard drive and does
+            not affect the training.
+            During the training one main thread and one thread for each GPU are used.
+        """
         self.iterations = iterations
         self.depth = depth
         self.learning_rate = learning_rate

etna/models/sklearn.py

@@ -27,13 +27,29 @@ class SklearnPerSegmentModel(PerSegmentModel):
     """Class for holding per segment Sklearn model."""
 
     def __init__(self, regressor: RegressorMixin):
+        """
+        Create instance of SklearnPerSegmentModel with given parameters.
+
+        Parameters
+        ----------
+        regressor:
+            sklearn model for regression
+        """
         super().__init__(base_model=_SklearnModel(regressor=regressor))
 
 
 class SklearnMultiSegmentModel(Model):
     """Class for holding Sklearn model for all segments."""
 
     def __init__(self, regressor: RegressorMixin):
+        """
+        Create instance of SklearnMultiSegmentModel with given parameters.
+
+        Parameters
+        ----------
+        regressor:
+            sklearn model for regression
+        """
         super().__init__()
         self._base_model = _SklearnModel(regressor=regressor)